Collecting gait information for evaluation and control of therapy

ABSTRACT

A medical device delivers a therapy to a patient. The medical device or another device may periodically determine an activity level or gait parameter of the patient, and associate each determined level or parameter with a current therapy parameter set. A value of at least one activity metric is determined for each of a plurality of therapy parameter sets based on the activity levels or parameters associated with that therapy parameter set. Whether the patient is currently experiencing or anticipated to experience gait freeze caused by their neurological disorder, such as Parkinson&#39;s disease, may also be determined. Gait freeze events may be associated with current therapy parameters and used to determine activity metric values. In some examples, the activity metric associated with certain therapy parameters may be presented to a user.

This application is a continuation of U.S. patent application Ser. No.11/691,423, filed Mar. 26, 2007, which claims the benefit of U.S.Provisional Application No. 60/785,658, filed Mar. 24, 2006. U.S. patentapplication Ser. No. 11/691,423 issued as U.S. Pat. No. 8,744,587 onJun. 3, 2014. The entire content of both of these applications isincorporated by reference herein.

TECHNICAL FIELD

The invention relates to medical devices and, more particularly, tomedical devices that deliver therapy.

BACKGROUND

In some cases, an ailment may affect a patient's activity level or rangeof activities by preventing the patient from being active. For example,chronic pain may cause a patient to avoid particular physicalactivities, or physical activity in general, where such activitiesincrease the pain experienced by the patient. Other ailments that mayaffect patient activity include movement or neurological disorders, suchas tremor or Parkinson's disease, which may result in irregular movementor activity, as well as a generally decreased level of activity.Further, other neurological disorders may affect a patient's physicalactivity. For example, epilepsy is an example of a neurological disorderthat may change or otherwise affect physical activity frequency ormagnitude of the patient. Occurring epileptic seizures, or the threat ofseizures, may deter physical activity. Additional neurological disordersmay include tremor, multiple sclerosis, or spasticity.

Neurological disorders may also include other disorders. The difficultywalking or otherwise moving experienced by patients with movementdisorders may cause such patients to avoid movement to the extentpossible. Further, mood or other psychological disorders, congestiveheart failure, or cardiac arrhythmia are other examples of disordersthat may generally cause a patient to be less active.

Drugs are often used to treat neurological disorders. In some cases,these ailments are treated via a medical device, such as an implantablemedical device (IMD). For example, patients may receive an implantableneurostimulator or drug delivery device to treat chronic pain, amovement disorder, a neurological disorder, or a mood disorder.Congestive heart failure and arrhythmia may be treated by, for example,a cardiac pacemaker or drug delivery device.

SUMMARY

In general, the invention is directed to techniques for evaluating atherapy delivered to a patient by a medical device based on patientactivity. More specifically, patient activity and/or gait may bedetected and used to evaluate or control delivery of therapy forpatients with movement disorders, such as Parkinson's disease. At anygiven time, the medical device delivers the therapy according to acurrent set of therapy parameters. The therapy parameters may changeover time such that the therapy is delivered according to a plurality ofdifferent therapy parameter sets. The medical device, or another device,periodically determines an activity level of the patient, and associateseach determined activity level with the current therapy parameter set.

An activity signal monitored according to embodiments the invention maybe indicative of patient gait in order to identify gait irregularityand/or gait freeze events. A value of at least one activity metric isdetermined for each of the therapy parameter sets based on the activitylevels and/or gait parameters associated with that parameter set. Forexample, gait parameters may be monitored with accelerometers or othersensors capable of measuring the gait of the patient.

A list of the therapy parameter sets and associated activity metrics ispresented to a user, such as a clinician, for evaluation of the relativeefficacy of the therapy parameter sets. The list may be orderedaccording to the activity metric values to aid in evaluation of thetherapy parameter sets. In this manner, the user may readily identifythe therapy parameter sets that support the highest activity levels forthe patient or reduce or eliminate gait irregularity or freeze of thepatient, and thereby evaluate the relative efficacy of the parametersets.

The therapy may be directed to treating any number of disorders. Forexample, the therapy may be directed to treating a non-respiratoryneurological disorder, such as a movement disorder or psychologicaldisorder. Example movement disorders for which therapy may be providedare Parkinson's disease, essential tremor and epilepsy. Non-respiratoryneurological disorders do not include respiratory disorders, such assleep apnea.

The therapy delivering medical device or another device may monitor atleast one signal that is generated by a sensor and varies as a functionof patient activity. For example, the device may monitor a signalgenerated by an accelerometer, a bonded piezoelectric crystal, a mercuryswitch, or a gyro. In some embodiments, the device may monitor a signalthat indicates a physiological parameter of the patient, which in turnvaries as a function of patient activity. For example, the device maymonitor a signal that indicates the heart rate, electrocardiogram (ECG)morphology, electroencephalogram (EEG) morphology, respiration rate,respiratory volume, core temperature, subcutaneous temperature, ormuscular activity of the patient.

The therapy delivering medical device or another device may periodicallydetermine an activity level of the patient based on the one or moresignals. In some embodiments, the device periodically determines anumber of activity counts based on the signals, and the number ofactivity counts is stored as the activity level. The number of activitycounts may be a number of threshold crossings by a signal generated by asensor such as an accelerometer or piezoelectric crystal during a sampleperiod, or a number of switch contacts indicated by the signal generatedby a sensor such as mercury switch during a sample period.

In some embodiments, the device may periodically determine a heart rate,measured value of one or more ECG morphological features, EEG signals,respiration rate, respiratory volume, core temperature, subcutaneoustemperature, and/or muscular activity level of the patient based on oneor more signals. The determined values of these parameters may be meanor median values. The device may compare a determined value of such aphysiological parameter to one or more thresholds to determine a numberof activity counts, which may be stored as a determined activity level.In other embodiments, the device may store the determined physiologicalparameter value as a determined activity level.

The use of activity counts, however, may allow the device to determinean activity level based on a plurality of signals. For example, thedevice may determine a first number of activity counts based on anaccelerometer signal and a second number of activity counts based on aheart rate determined at the time the accelerometer signal was sampled.The device may determine an activity level by calculating the sum oraverage, which may be a weighted sum or average, of first and secondactivity counts.

As mentioned above, the device may associate each determined activitylevel with a current set of therapy parameters and, for each of aplurality of therapy parameter sets used by the medical device overtime, a value of one or more activity metrics is determined. An activitymetric value may be, for example, a mean or median activity level, suchas an average number of activity counts per unit time. In otherembodiments, an activity metric value may be chosen from a predeterminedscale of activity metric values based on comparison of a mean or medianactivity level to one or more threshold values. The scale may benumeric, such as activity metric values from 1-10, or qualitative, suchas low, medium or high activity.

In some embodiments, each activity level associated with a therapyparameter set is compared with the one or more thresholds, andpercentages of time above and/or below the thresholds are determined asone or more activity metric values for that therapy parameter set. Inother embodiments, each activity level associated with a therapyparameter set is compared with a threshold, and an average length oftime that consecutively determined activity levels remain above thethreshold is determined as an activity metric value for that therapyparameter set. One or both of the medical device or another device, suchas a programming device or other computing device, may determine theactivity metric values as described herein.

Further, in some embodiments, activity levels or activity sensor signalsmay be compared to thresholds or templates, or otherwise analyzed, todetermine the regularity of gait or identify the occurrence of a gaitfreeze event. In such embodiments, activity sensor signals may includesignals that reflect gross anatomical movement, muscle activity, orfootfalls of the patient, such as accelerometer signals or piezoelectriccrystal signals. A value indicative of the regularity of the gait may bedetermined, and associated with a therapy parameter set that wascurrently being used to control delivery of therapy when the gaitregularity value was determined. When a gait freeze event is identified,the occurrence of a gait freeze event may be associated with a therapyparameter set that was current being used to control delivery of therapywhen the gait freeze event occurred.

The computing device or, in some external medical device embodiments,the medical device, presents a list of the plurality of parameter sets,associated activity metric values, such as gait regularity or a numbergait freeze events, via a display. The computing device may order thelist according to the activity metric values. Where values aredetermined for a plurality of activity metrics for each of the therapyparameter sets, the computing device may order the list according to thevalues of a user selected one of the activity metrics. The computingdevice may also present other activity information to a user, such as atrend diagram of activity, gait regularity, or gait freeze events overtime, or a histogram or pie chart illustrating percentages of time thatactivity levels were within certain ranges. The computing device maygenerate such charts or diagrams using activity levels associated with aparticular one of the therapy parameter sets, or all of the determinedactivity levels.

In one embodiment, the invention is directed to a method that includesdelivering a therapy from a medical device to a patient to treat amovement disorder, monitoring gait of the patient based on a signalgenerated by a sensor that varies as a function of patient activity and,for each of a plurality of therapy parameter sets used by the medicaldevice to control delivery of the therapy to the patient, determining avalue of at least one metric based on the gait of the patient duringdelivery of the therapy by the medical device according to the therapyparameter set.

In another embodiment, the invention is directed to a system thatincludes a medical device that delivers at least one of a movementdisorder therapy, Parkinson's disease therapy, epilepsy therapy, tremortherapy, or deep brain stimulation to a patient. The system furthercomprises an implanted sensor that generates a signal that varies as afunction of activity of the patient, and a processor. The processormonitors gait of the patient based on the signal and, for each of aplurality of therapy parameter sets used by the medical device tocontrol delivery of the therapy to the patient, determines a value of atleast one metric based on the gait of the patient during delivery of thetherapy by the medical device according to the therapy parameter set.

In another embodiment, the invention is directed to a computer-readablemedium including instructions that cause a processor to monitor gait ofa patient based on a signal generated by a sensor that varies as afunction of patient activity and, for each of a plurality of therapyparameter sets used by a medical device to control delivery of a therapyto the patient, determine a value of at least one metric based on thegait of the patient during delivery of the therapy by the medical deviceaccording to the therapy parameter set, wherein the therapy comprises atleast one of a movement disorder therapy, Parkinson's disease therapy,epilepsy therapy, tremor therapy, or deep brain stimulation.

In another embodiment, the invention is directed to a method thatincludes delivering a therapy from an implantable medical device to apatient to treat a movement disorder, monitoring gait of the patientwith the implantable medical device based on a signal generated by asensor that varies as a function of patient activity, and detecting agait freeze event with the implantable medical device based on thesignal.

In another embodiment, the invention is directed to a system comprisingan implantable medical device that delivers at least one of a movementdisorder therapy, Parkinson's disease therapy, epilepsy therapy, tremortherapy, or deep brain stimulation to a patient. The system furthercomprises an implanted sensor that generates a signal that varies as afunction of activity of the patient, and a processor that monitors gaitof the patient based on the signal, and detects a gait freeze eventbased on the signal.

The invention is capable of providing one or more advantages. Forexample, a medical system according to the invention may provide aclinician with an objective indication of the efficacy of different setsof therapy parameters to improve gait or reduce the frequency of gaitfreeze events in patients with Parkinson's disease. Further, bydisplaying therapy parameter sets and associated activity metric valuesin an ordered and, in some cases, sortable list, the medical system mayallow the clinician to more easily compare the relative efficacies of aplurality of therapy parameter sets. The medical system may beparticularly useful in the context of trial neurostimulation or drugdelivery, where the patient is encouraged to try a plurality of therapyparameter sets to allow the patient and clinician to identifyefficacious therapy parameter sets. Further, the medical system may beparticularly useful in the context of movement disorders, which mayimpact both the overall level of patient activity, and also result inirregular movements with activity.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIGS. 1A and 1B are conceptual diagrams illustrating example systemsthat include an implantable medical device that collects activityinformation according to the invention.

FIGS. 2A and 2B are block diagrams further illustrating the examplesystems and implantable medical devices of FIGS. 1A and 1B.

FIG. 3 is a logic diagram illustrating an example circuit that detectsthe sleep state of a patient from the electroencephalogram (EEG) signal.

FIG. 4 is a block diagram illustrating an example memory of theimplantable medical devices of FIGS. 1A and 1B.

FIG. 5 is a flow diagram illustrating an example method for collectingactivity information that may be employed by an implantable medicaldevice.

FIG. 6 is a block diagram illustrating an example clinician programmer.

FIG. 7 illustrates an example list of therapy parameter sets andassociated activity metric values that may be presented by a clinicianprogrammer.

FIG. 8 is a flow diagram illustrating an example method for displaying alist of therapy parameter sets and associated activity metric valuesthat may be employed by a clinician programmer.

FIG. 9 is a conceptual diagram illustrating a monitor that monitorsvalues of one or more physiological parameters of the patient.

FIG. 10 is a block diagram illustrating an example system includingimplantable medical device that collects activity information, andfurther identifies and responds to gait freeze events.

FIG. 11 is a flow diagram illustrating an example method for monitoringgait regularity, and identifying and responding to gait freeze events.

FIG. 12 illustrates an example list of therapy parameter sets andassociated activity metric values relating to patient gait which may bepresented by a clinician programmer.

FIG. 13 is a conceptual diagram illustrating a monitor that monitorsvalues of one or more accelerometers.

FIG. 14 is a flow diagram illustrating monitoring the heart rate andbreathing rate of a patient by measuring cerebral spinal fluid pressure.

DETAILED DESCRIPTION

FIGS. 1A and 1B are conceptual diagrams illustrating example systems 10Aand 10B (collectively “systems 10”) that respectively include animplantable medical device (IMD) 14A or 14B (collectively “IMDs 14”)that collect information relating to the activity of a respective one ofpatients 12A and 12 B (collectively “patients 12”). In the illustratedexample systems 10, IMDs 14 take the form of an implantableneurostimulator that delivers neurostimulation therapy in the form ofelectrical pulses to patients 12. However, the invention is not limitedto implementation via an implantable neurostimulators. For example, insome embodiments of the invention, IMD 14 may take the form of animplantable pump or implantable cardiac rhythm management device, suchas a pacemaker, that collects activity information. Further, theinvention is not limited to implementation via an IMD. In other words,any implantable or external device may collect activity informationaccording to the invention.

In the illustrated examples of FIGS. 1A and 1B, IMDs 14A and 14Brespectively deliver neurostimulation therapy to patients 12A and 12Bvia leads 16A and 16B, and leads 16C and 16D (collectively “leads 16”),respectively. Leads 16A and 16B may, as shown in FIG. 1A, be implantedproximate to the spinal cord 18 of patient 12A, and IMD 14A may deliverspinal cord stimulation (SCS) therapy to patient 12A in order to, forexample, reduce pain experienced by patient 12A. However, the inventionis not limited to the configuration of leads 16A and 16B shown in FIG.1A or the delivery of SCS or other pain therapies.

For example, in another embodiment, illustrated in FIG. 1B, leads 16Cand 16D may extend to brain 19 of patient 12B, e.g., through cranium 17of patient. IMD 14B may deliver deep brain stimulation (DBS) or corticalstimulation therapy to patient 12 to treat any of a variety ofnon-respiratory neurological disorders, such as movement disorders orpsychological disorders. Example therapies may treat tremor, Parkinson'sdisease, spasticity, epilepsy, depression or obsessive-compulsivedisorder. As illustrated in FIG. 1B, leads 16C and 16D may be coupled toIMD 14B via one or more lead extensions 15. Leads 16C and 16D may beplaced within the brain of patient 12B according to commonly used DBSapplications.

As further examples, one or more leads 16 may be implanted proximate tothe pelvic nerves (not shown) or stomach (not shown), and an IMD 14 maydeliver neurostimulation therapy to treat incontinence or gastroparesis.Additionally, leads 16 may be implanted on or within the heart to treatany of a variety of cardiac disorders, such as congestive heart failureor arrhythmia, or may be implanted proximate to any peripheral nerves totreat any of a variety of disorders, such as peripheral neuropathy orother types of chronic pain.

The illustrated numbers and locations of leads 16 are merely examples.Embodiments of the invention may include any number of lead implanted atany of a variety of locations within a patient. Furthermore, theillustrated number and location of IMDs 14 are merely examples. IMDs 14may be located anywhere within patient according to various embodimentsof the invention. For example, in some embodiments, an IMD 14 may beimplanted on or within cranium 17 for delivery of therapy to brain 19,or other structure of the head of the patient 12.

IMDs 14 deliver therapy according to a set of therapy parameters, i.e.,a set of values for a number of parameters that define the therapydelivered according to that therapy parameter set. In embodiments whereIMDs 14 deliver neurostimulation therapy in the form of electricalpulses, the parameters for each therapy parameter set may includevoltage or current pulse amplitudes, pulse widths, pulse rates,duration, duty cycle and the like. Further, each of leads 16 includeselectrodes (not shown in FIGS. 1A and 1B), and a therapy parameter setmay include information identifying which electrodes have been selectedfor delivery of pulses, and the polarities of the selected electrodes.In embodiments in which IMDs 14 deliver other types of therapies,therapy parameter sets may include other therapy parameters, such asdrug concentration and drug flow rate in the case of drug deliverytherapy. Therapy parameter sets used by IMDs 14 may include a number ofparameter sets programmed by one or more clinicians (not shown), andparameter sets representing adjustments made by patients 12 to thesepreprogrammed sets.

IMDs 14 may deliver electrical stimulation to treat and/or reduce thesymptoms of any of a variety of non-respiratory neurological disorders(hereinafter referred to as only “neurological disorders”). Theseneurological disorders may not include respiratory disorders, such ascentral sleep apnea. For example, IMD 14B may deliver DBS in order to,for example, reduce the frequency and severity of epileptic seizuresexperienced by patient 12B with epilepsy. As other examples, IMD 14B maydeliver DBS in order to reduce the symptoms of a movement disorder orpsychological disorder, such as tremor, Parkinson's disease, multiplesclerosis, spasticity, depression, mania, bipolar disorder, orobsessive-compulsive disorder. Additionally, IMD 14A may deliver SCS, orIMD 14B may deliver DBS to treat chronic pain or other non-respiratoryneurological disorders, e.g., excluding for example central sleep apnea.

Each of systems 10 may also include a clinician programmer 20(illustrated as part of system 10A in FIG. 1A). The clinician may useclinician programmer 20 to program therapy for patient 12A, e.g.,specify a number of therapy parameter sets and provide the parametersets to IMD 14A. The clinician may also use clinician programmer 20 toretrieve information collected by IMD 14A. The clinician may useclinician programmer 20 to communicate with IMD 14A both during initialprogramming of IMD 14A, and for collection of information and furtherprogramming during follow-up visits.

Clinician programmer 20 may, as shown in FIG. 1A, be a handheldcomputing device. Clinician programmer 20 includes a display 22, such asa LCD or LED display, to display information to a user. Clinicianprogrammer 20 may also include a keypad 24, which may be used by a userto interact with clinician programmer 20. In some embodiments, display22 may be a touch screen display, and a user may interact with clinicianprogrammer 20 via display 22. A user may also interact with clinicianprogrammer 20 using peripheral pointing devices, such as a stylus ormouse. Keypad 24 may take the form of an alphanumeric keypad or areduced set of keys associated with particular functions.

Systems 10 may also includes a patient programmer 26 (illustrated aspart of system 10A in FIG. 1A), which also may, as shown in FIG. 1A, bea handheld computing device. Patient 12A may use patient programmer 26to control the delivery of therapy by IMD 14A. For example, usingpatient programmer 26, patient 12A may select a current therapyparameter set from among the therapy parameter sets preprogrammed by theclinician, or may adjust one or more parameters of a preprogrammedtherapy parameter set to arrive at the current therapy parameter set.

Patient programmer 26 may include a display 28 and a keypad 30, to allowpatient 12A to interact with patient programmer 26. In some embodiments,display 28 may be a touch screen display, and patient 12A may interactwith patient programmer 26 via display 28. Patient 12A may also interactwith patient programmer 26 using peripheral pointing devices, such as astylus, mouse, or the like.

Clinician and patient programmers 20, 26 are not limited to thehand-held computer embodiments illustrated in FIG. 1A. Programmers 20,26 according to the invention may be any sort of computing device. Forexample, a programmer 20, 26 according to the invention may be atablet-based computing device, a desktop computing device, or aworkstation.

IMDs 14, clinician programmers 20 and patient programmers 26 may, asshown in FIG. 1A, communicate via wireless communication. Clinicianprogrammer 20 and patient programmer 26 may, for example, communicatevia wireless communication with IMD 14A using radio frequency (RF) orinfrared telemetry techniques known in the art. Clinician programmer 20and patient programmer 26 may communicate with each other using any of avariety of local wireless communication techniques, such as RFcommunication according to the 802.11 or Bluetooth specification sets,infrared communication according to the IRDA specification set, or otherstandard or proprietary telemetry protocols.

Clinician programmer 20 and patient programmer 26 need not communicatewirelessly, however. For example, programmers 20 and 26 may communicatevia a wired connection, such as via a serial communication cable, or viaexchange of removable media, such as magnetic or optical disks, ormemory cards or sticks. Further, clinician programmer 20 may communicatewith one or both of IMD 14 and patient programmer 26 via remotetelemetry techniques known in the art, communicating via a local areanetwork (LAN), wide area network (WAN), public switched telephonenetwork (PSTN), or cellular telephone network, for example.

As mentioned above, IMDs 14 collect patient activity information.Specifically, as will be described in greater detail below, IMDs 14 mayperiodically determine an activity level of patient 12 based on a signalthat varies as a function of patient activity. IMDs 14 may associateeach determined activity level with the therapy parameter set that iscurrently active when the activity level is determined. An activitylevel may comprise, for example, a number of activity counts, or a valuefor a physiological parameter that reflects patient activity.

Over time, IMDs 14 use a plurality of therapy parameter sets to deliverthe therapy to patient 12. A processor within IMDs 14 or another device,such as one of programmers 20, 26 or another computing device,determines a value of one or more activity metrics for each of theplurality of therapy parameter sets based on the activity levelsassociated with the therapy parameter sets. An activity metric value maybe, for example, a mean or median activity level, such as an averagenumber of activity counts per unit time. In other embodiments, anactivity metric value may be chosen from a predetermined scale ofactivity metric values based on a comparison of a mean or medianactivity level to one or more threshold values. The scale may benumeric, such as activity metric values from 1-10, or qualitative, suchas low, medium or high activity.

In some embodiments, each activity level associated with a therapyparameter set is compared with the one or more thresholds, andpercentages of time above and/or below the thresholds are determined asone or more activity metric values for that therapy parameter set. Inother embodiments, each activity level associated with a therapyparameter set is compared with a threshold, and an average length oftime that consecutively determined activity levels remain above thethreshold is determined as an activity metric value for that therapyparameter set.

Furthermore, in some embodiments, gait regularity or a number of gaitfreeze events occurring during delivery of therapy according to aparameter set may be an activity metric value for the therapy parameterset. In some embodiments, activity levels or activity sensor signals maybe compared to thresholds or templates, or otherwise analyzed, todetermine gait regularity or identify the occurrence of a gait freezeevent. In such embodiments, activity sensor signals may include signalsthat reflect gross anatomical movement, muscle activity, or footfalls ofthe patient, such accelerometer signals or piezoelectric crystalsignals. A value indicative of the regularity of the gait may bedetermined, and associated with a therapy parameter set that wascurrently being used to control delivery of therapy when the gaitregularity value was determined. When a gait freeze event is identified,the occurrence of a gait freeze event may be associated with a therapyparameter sets that was current being used to control delivery oftherapy when the gait freeze event occurred.

In some embodiments, a plurality of activity metric values aredetermined for each of the plurality of therapy parameter sets. In suchembodiments, an overall activity metric value may be determined. Forexample, the plurality of individual activity metric values may be usedas indices to identify an overall activity metric value from a look-uptable. The overall activity metric may selected from a predeterminedscale of activity metric values, which may be numeric, such as activitymetric values from 1-10, or qualitative, such as low, medium or highactivity.

One or more of IMDs 14, programmers 20, 26, or another computing devicemay determine the activity metric values as described herein. In someembodiments, IMDs 14 determine and store activity metric values for eachof a plurality of therapy parameter sets, and provide informationidentifying the therapy parameter sets and the associated activitymetric values to clinician programmers 20. In other embodiments, IMDs 14provide information identifying the therapy parameter sets andassociated activity levels or signals to clinician programmers 20, andclinician programmers 20 determines the activity metric values for eachof the therapy parameter sets.

In either of these embodiments, clinician programmers 20 present a listof the plurality of parameter sets and associated activity metric valuesto the clinician via display 22. Programmers 20 may order the listaccording to the activity metric values. Where values are determined fora plurality of activity metrics for each of the therapy parameter sets,programmers 20 may order the list according to the values of one of theactivity metrics that is selected by the clinician. Programmers 20 mayalso present other activity information to the clinician, such as atrend diagram of activity over time, or a histogram or pie chartillustrating percentages of time that activity levels were withincertain ranges. Programmers 20 may generate such charts or diagramsusing activity levels associated with a particular one of the therapyparameter sets, or all of the activity levels determined by IMDs 14.

However, the invention is not limited to embodiments that includeprogrammers 20, or embodiments in which programmers 20 presents activityinformation to the clinician. For example, in some embodiments,programmers 26 present activity information as described herein to oneor both of the clinician and patients 12. Further, in some embodiments,an external medical device comprises a display. In such embodiments, theexternal medical device may both determine activity metric values forthe plurality of therapy parameter sets, and present the list of therapyparameter sets and activity metric values. Additionally, in someembodiments, any type of computing device, e.g., personal computer,workstation, or server, may identify activity levels, determine activitymetric values, and/or present a list to a patient or clinician.

Further, the invention is not limited to embodiments in which a medicaldevice collects activity signals or determines activity levels. Forexample, in some embodiments, IMDs 14 may instead periodically recordsamples of one or more signals that vary as a function of patientactivity, and associate the samples with a current therapy parameterset. In such embodiments, programmers 20 or 26, or another computingdevice, may receive information identifying a plurality of therapyparameter sets and the samples associated with the parameter sets, maydetermine activity levels and/or metric values based on the samples.

FIGS. 2A and 2B are block diagrams further illustrating systems 10A and10B. In particular, FIG. 2A illustrates an example configuration of IMD14A and leads 16A and 16B. FIG. 2B illustrates an example configurationof IMD 14B and leads 16C and 16D. FIGS. 2A and 2B also illustratesensors 40A and 40B (collectively “sensors 40”) that generate signalsthat vary as a function of patient activity. As will be described ingreater detail below, IMDs 14 monitor the signals, and may periodicallydetermine an activity level or gait parameter, or identify a gait freezeevent, based on the signals.

IMDs 14 may deliver neurostimulation therapy via electrodes 42A-D oflead 16A and electrodes 42E-H of lead 16B, while IMD 14B deliversneurostimulation via electrodes 42I-L of lead 16C and electrodes 42 M-Pof lead 16D (collectively “electrodes 42”). Electrodes 42 may be ringelectrodes. The configuration, type and number of electrodes 42illustrated in FIGS. 2A and 2B are merely exemplary. For example, leads16 may each include eight electrodes 42, and the electrodes 42 need notbe arranged linearly on each of leads 16.

In each of systems 10A and 10B, electrodes 42 are electrically coupledto a therapy delivery module 44 via leads 16. Therapy delivery module 44may, for example, include an output pulse generator coupled to a powersource such as a battery. Therapy delivery module 44 may deliverelectrical pulses to a patient 12 via at least some of electrodes 42under the control of a processor 46, which controls therapy deliverymodule 44 to deliver neurostimulation therapy according to a currenttherapy parameter set. However, the invention is not limited toimplantable neurostimulator embodiments or even to IMDs that deliverelectrical stimulation. For example, in some embodiments a therapydelivery module 44 of an IMD may include a pump, circuitry to controlthe pump, and a reservoir to store a therapeutic agent for delivery viathe pump.

Processor 46 may include a microprocessor, a controller, a digitalsignal processor (DSP), an application specific integrated circuit(ASIC), a field-programmable gate array (FPGA), discrete logiccircuitry, or the like. Memory 48 may include any volatile,non-volatile, magnetic, optical, or electrical media, such as a randomaccess memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM),electrically-erasable programmable ROM (EEPROM), flash memory, and thelike. In some embodiments, memory 48 stores program instructions that,when executed by processor 46, cause IMD 14 and processor 46 to performthe functions attributed to them herein.

Each of sensors 40 generates a signal that varies as a function of apatient 12 activity. IMDs 14 may include circuitry (not shown) thatconditions the signals generated by sensors 40 such that they may beanalyzed by processor 46. For example, IMDs 14 may include one or moreanalog to digital converters to convert analog signals generated bysensors 40 into digital signals usable by processor 46, as well assuitable filter and amplifier circuitry. Although shown as including twosensors 40, systems 10A and 10B may include any number of sensors.

Further, as illustrated in FIGS. 2A and 2B, sensors 40 may be includedas part of IMDs 14, or coupled to IMDs 14 via leads 16. Sensors 40 maybe coupled to IMD 14 via therapy leads 16A-16D, or via other leads 16,such as lead 16E depicted in FIGS. 2A and 2B. In some embodiments, asensor 40 located outside of an IMD 14 may be in wireless communicationwith processor 46. Wireless communication between sensors 40 and IMDs 14may, as examples, include RF communication or communication viaelectrical signals conducted through the tissue and/or fluid of apatient 12.

A sensor 40 may be, for example, an accelerometer, a bondedpiezoelectric crystal, a mercury switch, or a gyro that generates asignal as a function of patient activity, e.g., body motion, footfallsor other impact events, and the like. Processor 46 may determine anactivity level based on a signal generated by one of these types ofsensors 40 by sampling the signal and determining a number of activitycounts during the sample period. Processor 46 may then store thedetermined number of activity counts in memory 48 as an activity level.

For example, processor 46 may compare the sample of a signal generatedby an accelerometer or piezoelectric crystal to one or more amplitudethresholds stored within memory 48. Processor 46 may identify eachthreshold crossing as an activity count. Where processor 46 compares thesample to multiple thresholds with varying amplitudes, processor 46 mayidentify crossing of higher amplitude thresholds as multiple activitycounts. Using multiple thresholds to identify activity counts, processor46 may be able to more accurately determine the extent of patientactivity for both high impact, low frequency and low impact, highfrequency activities. In embodiments in which a sensor 40 takes the formof a mercury switch, processor 46 may identify the number of switchcontacts indicated during the sample period as the number of activitycounts.

In embodiments in which a sensor 40 comprises an accelerometer orpiezoelectric crystal, IMDs 14 may include a filter (not shown), orprocessor 46 may apply a digital filter, that passes a band fromapproximately 0.1 Hz to 10 Hz. The filter may reduce noise in thesignal, and pass the portion of the signal that reflects patientactivity.

In some embodiments, sensors 40 may generate a signal both as a functionof patient activity and patient posture. For example, accelerometers,gyros, or magnetometers may generate signals that indicate both theactivity and the posture of a patient 12. As will be described below,posture may be monitored to confirm a specific activity event, gaitfreeze, as will be discussed in greater detail below.

In some embodiments, in order to identify posture, sensors 40 such asaccelerometers may be oriented substantially orthogonally with respectto each other. In addition to being oriented orthogonally with respectto each other, each of sensors 40 used to detect the posture of apatient 12 may be substantially aligned with an axis of the body of apatient 12. When accelerometers, for example, are aligned in thismanner, the magnitude and polarity of DC components of the signalsgenerate by the accelerometers indicate the orientation of the patientrelative to the Earth's gravity, e.g., the posture of a patient 12.Further information regarding use of orthogonally aligned accelerometersto determine patient posture may be found in a commonly assigned U.S.Pat. No. 5,593,431, which issued to Todd J. Sheldon.

Other sensors 40 that may generate a signal that indicates the postureof a patient 12 include electrodes that generate a signal as a functionof electrical activity within muscles of a patient 12, e.g., anelectromyogram (EMG) signal, or a bonded piezoelectric crystal thatgenerates a signal as a function of contraction of muscles. Electrodesor bonded piezoelectric crystals may be implanted in the legs, buttocks,chest, abdomen, or back of a patient 12, and coupled to IMDs 14wirelessly or via one or more leads 16. Alternatively, electrodes may beintegrated in a housing of the IMD or piezoelectric crystals may bebonded to the housing when IMD is implanted in the buttocks, chest,abdomen, or back of a patient 12. The signals generated by such sensorswhen implanted in these locations may vary based on the posture of apatient 12, e.g., may vary based on whether the patient is standing,sitting, or lying down.

Further, the posture of a patient 12 may affect the thoracic impedanceof the patient. Consequently, sensors 40 may include an electrode pair,including one electrode integrated with the housing of IMDs 14 and oneof electrodes 42, that generates a signal as a function of the thoracicimpedance of a patient 12, and processor 46 may detect the posture orposture changes of a patient 12 based on the signal. The electrodes ofthe pair may be located on opposite sides of the patient's thorax. Forexample, the electrode pair may include one of electrodes 42 locatedproximate to the spine of a patient for delivery of SCS therapy, and IMD14A with an electrode integrated in its housing may be implanted in theabdomen or chest of patient 12A. As another example, IMD 14B may includeelectrodes implanted to detect thoracic impedance in addition to leads16 implanted within the brain of patient 12B. The posture or posturechanges may affect the delivery of DBS or SCS therapy to either patient12A or 12B for the treatment of any type of neurological disorder, andmay also be used to detect patient sleep, as described herein.

Additionally, changes of the posture of a patient 12 may cause pressurechanges with the cerebrospinal fluid (CSF) of the patient. Consequently,sensors 40 may include pressure sensors coupled to one or moreintrathecal or intracerebroventricular catheters, or pressure sensorscoupled to IMDs 14 wirelessly or via one of leads 16. CSF pressurechanges associated with posture changes may be particularly evidentwithin the brain of the patient, e.g., may be particularly apparent inan intracranial pressure (ICP) waveform.

In some embodiments, processor 46 may monitor a signal that indicates aphysiological parameter of a patient 12, which in turn varies as afunction of patient activity. For example, processor 46 may monitor asignal that indicates the heart rate, ECG morphology, EEG morphology,respiration rate, respiratory volume, core temperature, subcutaneoustemperature, or muscular activity of the patient. In such embodiments,processor 46 may periodically determine the heart rate, measured valueof one or more ECG morphological features, respiration rate, respiratoryvolume, core temperature, subcutaneous temperature, or muscular activitylevel of a patient 12 based on the signal. The determined values ofthese parameters may be mean or median values.

Sensors 40 may include electrodes located on leads 16 or integrated aspart of the housing of IMDs 14 that generates an electrogram signal as afunction of electrical activity of the heart of a patient 12, andprocessor 46 may periodically determine the heart rate of a patient 12based on the electrogram signal. In other embodiments, a sensor 40 mayinclude an acoustic sensor within IMDs 14, a pressure sensor within thebloodstream or cerebrospinal fluid of a patient 12, or a temperaturesensor located within the bloodstream of the patient 12. The signalsgenerated by such sensors 40 may vary as a function of contraction ofthe heart of a patient 12, and can be used by processor 46 toperiodically determine the heart rate of the patient 12.

In some embodiments, processor 46 may detect, and measure values for oneor more ECG morphological features within an electrogram generated byelectrodes as described above. ECG morphological features may vary in amanner that indicates the activity level of patient. For example, theamplitude of the ST segment of the ECG may increase with increasedpatient activity. Further, the amplitude of QRS complex or T-wave mayincrease, and the widths of the QRS complex and T-wave may decrease withincreased patient activity. The QT interval and the latency of an evokedresponse may decrease with increased patient activity, and the amplitudeof the evoked response may increase with increased patient activity.

Sensors 40 may include an electrode pair, including one electrodeintegrated with the housing of IMDs 14 and one of electrodes 42, asdescribed above. In some embodiments, such an electrode pair maygenerate a signal as a function of the thoracic impedance of a patient12, which varies as a function of respiration by the patient 12. Inother embodiments, sensors 40 may include a strain gauge, bondedpiezoelectric element, or pressure sensor within the blood orcerebrospinal fluid that generates a signal that varies based on patientrespiration. Processor 46 may monitor the signals generated by suchsensors 40 to periodically determine a respiration rate and/orrespiratory volume of a patient 12. An electrogram generated byelectrodes as discussed above may also be modulated by patientrespiration, and processor 46 may use the electrogram as an indirectrepresentation of respiration rate.

In some embodiments, sensors 40 may include one or more electrodes thatgenerate an electromyogram (EMG) signal as a function of muscleelectrical activity. The amplitude and/or frequency of an EMG signal mayvary based on the activity level of a patient. The electrodes may be,for example, located in the legs, abdomen, chest, back or buttocks of apatient 12 to detect muscle activity associated with walking, running,or the like. The electrodes may be coupled to IMDs 14 wirelessly or byleads 16 or, if IMDs 14 are implanted in these locations, integratedwith a housing of a respective one of IMDs 14.

However, bonded piezoelectric crystals located in these areas generatesignals as a function of muscle contraction in addition to body motion,footfalls or other impact events. Consequently, use of bondedpiezoelectric crystals to detect activity of a patient 12 may bepreferred in some embodiments in which it is desired to detect muscleactivity in addition to body motion, footfalls, or other impact events.Bonded piezoelectric crystals may be coupled to IMDs 14 wirelessly orvia respective leads 16, or piezoelectric crystals may be bonded to thecan of IMDs 14 when the IMD is implanted in these areas, e.g., in theback, chest buttocks or abdomen of a patient 12.

In alternative embodiments, sensors 40 may be configured for placementwithin or around the brain of a patient 12 to detect the onset,magnitude, or duration of a neurological disorder. For example, sensors40 may detect the onset of an epileptic seizure and track the durationand extent of the seizure. IMDs 14 may compare neurological events tophysical activity to determine how the neurological events affectphysical activity. IMDs 14 may also initiate or change electricalstimulation when a neurological event is detected.

Further, sensors 40 may include any of a variety of known temperaturesensors to generate a signal as a function of a core or subcutaneoustemperature of a patient 12. Core or subcutaneous temperature may varyas a function of the activity level of a patient 12. Such temperaturesensors may be incorporated within the housing of IMDs 14, or coupled toIMDs 14 wirelessly or via leads.

In some embodiments, processor 46 compares a determined value of such aphysiological parameter to one or more thresholds or a look-up tablestored in memory to determine a number of activity counts, and storesthe determined number of activity counts in memory 48 as a determinedactivity level. In other embodiments, processor 46 may store thedetermined physiological parameter value as a determined activity level.The use of activity counts, however, may allow processor 46 to determinean activity level based on a plurality of signals generated by aplurality of sensors 40. For example, processor 46 may determine a firstnumber of activity counts based on a sample of an accelerometer signaland a second number of activity counts based on a heart rate determinedfrom an electrogram signal at the time the accelerometer signal wassampled. Processor 46 may determine an activity level by calculating thesum or average, which may be a weighted sum or average, of first andsecond activity counts.

Processor 46 may record activity levels continuously or periodically,e.g., one sample every minute or continuously for ten minutes each hour.In some embodiments, processor 46 limits recording of activity levels torelevant time periods, i.e., when a patient 12 is awake or likely to beawake, and therefore likely to be active. For example, patient mayindicate via patient programmer 26 when patient is attempting to sleepor awake. Processor 46 may receive these indications via a telemetrycircuit 50 of IMDs 14, and may suspend or resume recording of activitylevels based on the indications. In other embodiments, processor 46 maymaintain a real-time clock, and may record activity levels based on thetime of day indicated by the clock, e.g., processor 46 may limitactivity level recording to daytime hours.

Further, processor 46 may determine when a patient is asleep, attemptingto sleep, or awake by monitoring one or more physiological parameters ofthe patient based on the signals generated by sensors 40. Processor 46may for example, limit activity monitoring to times when the patient isnot asleep or attempting to sleep. For example, processor 46 maydetermine when patient 12 is attempting to sleep by monitoring theposture of patient 12 to determine when patient 12 is recumbent usingany of the posture monitoring sensors 40 or techniques described above.As an example, sensors 40 may include a plurality of orthogonallyarranged accelerometers, as discussed above, and processor 46 maymonitor the DC components of the signals generated by the accelerometersto determine when patient is recumbent.

In other embodiments, processor 46 determines when a patient 12 isattempting to fall asleep based on the level of melatonin in a bodilyfluid. In such embodiments, a sensor 40 may take the form of a chemicalsensor that is sensitive to the level of melatonin or a metabolite ofmelatonin in the bodily fluid, and estimate the time that the patient 12will attempt to fall asleep based on the detection. For example,processor 46 may compare the melatonin level or rate of change in themelatonin level to a threshold level stored in memory 48, and identifythe time that threshold value is exceeded. Processor 46 may identify thetime that patient 12 is attempting to fall asleep as the time that thethreshold is exceeded, or some amount of time after the threshold isexceeded. Any of a variety of combinations or variations of theabove-described techniques may be used to determine when a patient 12 isattempting to fall asleep, and a specific one or more techniques may beselected based on the sleeping and activity habits of a particularpatient.

In order to determine whether a patient 12 is asleep, processor 46 maymonitor any one or more physiological parameters that discernibly changewhen the patient 12 falls asleep, such as activity level, posture, heartrate, ECG morphology, respiration rate, respiratory volume, bloodpressure, blood oxygen saturation, partial pressure of oxygen withinblood, partial pressure of oxygen within cerebrospinal fluid, muscularactivity and tone, core temperature, subcutaneous temperature, arterialblood flow, brain electrical activity, eye motion, and galvanic skinresponse. Processor 46 may additionally or alternatively monitor thevariability of one or more of these physiological parameters, such asheart rate and respiration rate, which may discernible change when apatient 12 is asleep. Further details regarding monitoring physiologicalparameters to identify when a patient is attempting to sleep and whenthe patient is asleep may be found in a commonly-assigned and co-pendingU.S. patent application Ser. No. 11/691,405 by Kenneth Heruth et al.,entitled “DETECTING SLEEP,” and filed Mar. 26, 2007, and is incorporatedherein by reference in its entirety.

For example, in some embodiments, processor 46 may determine whether apatient 12 is asleep by analyzing an electroencephalogram (EEG) signalfrom the patient. EEG analysis is not limited to embodiments whichinclude leads 16 implanted on or within brain 19 of patient.Nonetheless, system 10B, illustrated in FIGS. 1B and 2B, is an exampleof a system that includes electrodes 42, located on or within the brainof patient 12B, that are coupled to IMD 14B. As shown in FIG. 2B,electrodes 42 may be selectively coupled to therapy module 44 or an EEGsignal module 54 by a multiplexer 52, which operates under the controlof processor 46. EEG signal module 54 receives signals from a selectedset of the electrodes 42 via multiplexer 52 as controlled by processor46. EEG signal module 54 may analyze the EEG signal for certain featuresindicative of sleep or different sleep states, and provide indicationsof relating to sleep or sleep states to processor 46. Thus, electrodes42 and EEG signal module 54 may be considered another sensor 40 insystem 10B. IMD 14B may include circuitry (not shown) that conditionsthe EEG signal such that it may be analyzed by processor 52. Forexample, IMD 14B may include one or more analog to digital converters toconvert analog signals received from electrodes 42 into digital signalsusable by processor 46, as well as suitable filter and amplifiercircuitry.

Processor 46 may direct EEG signal module to analyze the EEG signal todetermine whether patient 12B is sleeping, and such analysis may beconsidered alone or in combination with other physiological parametersto determine whether patient 12B is asleep. EEG signal module 60 mayprocess the EEG signals to detect when patient 12 is asleep using any ofa variety of techniques, such as techniques that identify whether apatient is asleep based on the amplitude and/or frequency of the EEGsignals. In some embodiments, the functionality of EEG signal module 54may be provided by processor 46, which, as described above, may includeone or more microprocessors, ASICs, or the like.

In other embodiments, processor 46 may record activity levels inresponse to receiving an indication from a patient 12 via patientprogrammer 26. For example, processor 46 may record activity levelsduring times when a patient 12 believes the therapy delivered by IMDs 14is ineffective and/or the symptoms experienced by a patient 12 haveworsened. In this manner, processor 46 may limit data collection toperiods in which more probative data is likely to be collected, andthereby conserve a battery and/or storage space within memory 48.

Further, as described above, the invention is not limited to embodimentsin which IMDs 14 determines activity levels. In some embodiments,processor 46 may periodically store samples of the signals generated bysensors 40 in memory 48, rather than activity levels, and may associatethose samples with the current therapy parameter set.

FIG. 3 is a logical diagram of an example circuit that detects sleepand/or sleep type of a patient based on the electroencephalogram (EEG)signal. The circuit may additionally or alternatively detect an awakestate when the patient is not in any sleep state. As shown in FIG. 3,module 49 may be integrated into an EEG signal module of an IMD 14 or aseparate implantable or external device capable of detecting an EEGsignal. An EEG signal detected by electrodes adjacent to the brain ofpatent 12 is transmitted into module 49 and provided to three channels,each of which includes a respective one of amplifiers 51, 67 and 83, andbandpass filters 53, 69 and 85. In other embodiments, a common amplifieramplifies the EEG signal prior to filters 53, 69 and 85.

Bandpass filter 53 allows frequencies between approximately 4 Hz andapproximately 8 Hz, and signals within the frequency range may beprevalent in the EEG during S1 and S2 sleep states. Bandpass filter 69allows frequencies between approximately 1 Hz and approximately 3 Hz,which may be prevalent in the EEG during the S3 and S4 sleep states.Bandpass filter 85 allows frequencies between approximately 10 Hz andapproximately 50 Hz, which may be prevalent in the EEG during REM sleep.Each resulting signal may then be processed to identify in which sleepstate a patient 12 is.

After bandpass filtering of the original EEG signal, the filteredsignals are similarly processed in parallel before being delivered tosleep logic module 99. For ease of discussion, only one of the threechannels will be discussed herein, but each of the filtered signalswould be processed similarly.

Once the EEG signal is filtered by bandpass filter 53, the signal isrectified by full-wave rectifier 55. Modules 57 and 59 respectivelydetermine the foreground average and background average so that thecurrent energy level can be compared to a background level at comparator63. The signal from background average is increased by gain 61 beforebeing sent to comparator 63, because comparator 63 operates in the rangeof millivolts or volts while the EEG signal amplitude is originally onthe order of microvolts. The signal from comparator 63 is indicative ofsleep stages S1 and S2. If duration logic 65 determines that the signalis greater than a predetermined level for a predetermined amount oftime, the signal is sent to sleep logic module 99 indicating that apatient 12 may be within the S1 or S2 sleep states. In some embodiments,as least duration logic 65, 81, 97 and sleep logic 99 may be embodied ina processor of the device containing EEG module 49.

Module 49 may detect all sleep types for a patient 12. Further, thebeginning of sleep may be detected by module 49 based on the sleep stateof a patient 12. Some of the components of module 49 may vary from theexample of FIG. 3. For example, gains 61, 77 and 93 may be provided fromthe same power source. Module 49 may be embodied as analog circuitry,digital circuitry, or a combination thereof.

In other embodiments, FIG. 3 may not need to reference the backgroundaverage to determine the current state of sleep of a patient 12.Instead, the power of the signals from bandpass filters 53, 69 and 85are compared to each other, and sleep logic module 99 determines whichthe sleep state of a patient 12 based upon the frequency band that hasthe highest power. In this case, the signals from full-wave rectifiers55, 71 and 87 are sent directly to a device that calculates the signalpower, such as a spectral power distribution module (SPD), and then tosleep logic module 99 which determines the frequency band of thegreatest power, e.g., the sleep state of a patient 12. In some cases,the signal from full-wave rectifiers 55, 71 and 87 may be normalized bya gain component to correctly weight each frequency band.

FIG. 4 illustrates memory 48 of an IMD 14 in greater detail. As shown inFIG. 4, memory 48 stores information describing a plurality of therapyparameter sets 60. Therapy parameter sets 60 may include parameter setsspecified by a clinician using clinician programmer 20. Therapyparameter sets 60 may also include parameter sets that are the result ofa patient 12 changing one or more parameters of one of the preprogrammedtherapy parameter sets. For example, a patient 12 may change parameterssuch as pulse amplitude, frequency or pulse width via patient programmer26.

Memory 48 also stores the activity levels 62 determined by processor 46.When processor 46 determines an activity level as discussed above,processor 46 associates the determined activity level with the currentone of therapy parameter sets 60, e.g., the one of therapy parametersets 60 that processor 46 is currently using to control delivery oftherapy by therapy module 44 to a patient 12. For example, processor 46may store determined activity levels 62 within memory 48 with anindication of the parameter sets 60 with which they are associated. Inother embodiments, processor 46 stores samples (not shown) of signalsgenerated by sensors 40 within memory 48 with an indication of theparameter sets 60 with which they are associated.

In some embodiments, processor 46 determines a value of one or moreactivity metrics for each of therapy parameter sets 60 based on theactivity levels 62 associated with the parameter sets 60. Processor 46may store the determined activity metric values 66 within memory 48 withan indication as to which of therapy parameter sets 60 the determinedvalues are associated with. For example, processor 46 may determine amean or median of activity levels associated with a therapy parameterset, and store the mean or median activity level as an activity metricvalue 66 for the therapy parameter set.

In embodiments in which activity levels 62 comprise activity counts,processor 46 may store, for example, an average number of activitycounts per unit time as an activity metric value. An average number ofactivity counts over a period substantially between ten and sixtyminutes, for example, may provide a more accurate indication of activitythan an average over shorter periods by ameliorating the effect oftransient activities on an activity signal or physiological parameters.For example, rolling over in bed may briefly increase the amplitude ofan activity signal and a heart rate, thereby confounding the activityanalysis.

In other embodiments, processor 46 may compare a mean or median activitylevel to one or more threshold values 64, and may select an activitymetric value from a predetermined scale of activity metric values basedon the comparison. The scale may be numeric, such as activity metricvalues from 1-10, or qualitative, such as low, medium or high activity.The scale of activity metric values may be, for example, stored as alook-up table within memory 48. Processor 46 stores the activity metricvalue 66 selected from the scale within memory 48.

In some embodiments, processor 46 compares each activity level 62associated with a therapy parameter set 60 to one or more thresholdvalues 64. Based on the comparison, processor 46 may determinepercentages of time above and/or below the thresholds, or withinthreshold ranges. Processor 46 may store the one or more determinedpercentages within memory 48 as one or more activity metric values 66for that therapy parameter set. In other embodiments, processor 46compares each activity level 62 associated with a therapy parameter set66 to a threshold values 64, and determines an average length of timethat consecutively recorded activity levels 62 remained above thethreshold as an activity metric value 66 for that therapy parameter set.

In some embodiments, processor 46 determines a plurality of activitymetric values for each of the plurality of therapy parameter sets, anddetermines an overall activity metric value for a parameter set based onthe values of the individual activity metrics for that parameter set.For example, processor 46 may use the plurality of individual activitymetric values as indices to identify an overall activity metric valuefrom a look-up table stored in memory 48. Processor 46 may select theoverall metric value from a predetermined scale of activity metricvalues, which may be numeric, such as activity metric values from 1-10,or qualitative, such as low, medium or high activity.

As shown in FIGS. 2A and 2B, IMDs 14 include a telemetry circuit 50, andprocessor 46 communicates with programmers 20, 26 via telemetry circuit50. In some embodiments, processor 46 provides information identifyingtherapy parameter sets 60 and activity metric values 66 associated withthe parameter sets to programmer 20, and programmer 20 displays a listof therapy parameter sets 60 and associated activity metric values 66.In other embodiments, as will be described in greater detail below,processor 46 does not determine activity metric values 66. Instead,processor 46 provides activity levels 62 to programmer 20 via telemetrycircuit 50, and programmer 20 determines activity metric values 66 fordisplay to the clinician. Further, in other embodiments, processor 46provides samples of signals generated by sensors 40 to programmer 20 viatelemetry circuit 50, and programmer 20 may determine both activitylevels 62 and activity metric values 66 based on the samples. Someexternal medical device embodiments of the invention include a display,and a processor of such an external medical device may both determineactivity metric values 66 and display a list of therapy parameter sets60 and associated activity metric values 66 to a clinician.

FIG. 5 is a flow diagram illustrating an example method for collectingactivity information that may be employed by an IMD 14. An IMD 14monitors one or more activity signals (70). For example, an IMD 14 maymonitor a signal generated by an accelerometer or piezoelectric crystal,and/or a signal that indicates a physiological parameter that varies asa function of patient activity, such heart rate, ECG morphology,respiration rate, respiratory volume, core temperature, subcutaneoustemperature, or muscle activity.

An IMD 14 determines an activity level 62 (72). For example, an IMD 14may determine a number of activity counts based on the one or moresignals, as described above. An IMD 14 identifies the current therapyparameter set 60, and associates the determined activity level 62 withthe current therapy parameter set 60 (74). For example, an IMD 14 maystore the determined activity level 62 in memory 48 with an indicationof the current therapy parameter set 60. An IMD 14 may then update oneor more activity metric values 66 associated with the current therapyparameter set 60, as described above (76).

An IMD 14 may periodically perform the example method illustrated inFIG. 5, e.g., may periodically monitor the activity signal (70),determine activity levels 62 (72), and associate the determined activitylevels 62 with a current therapy parameter set 60 (74). As describedabove, an IMD 14 may only perform the example method during daytimehours, or when a patient is awake and not attempting to sleep, and/oronly in response to an indication received from a patient 12 via patientprogrammer 20. An IMD 14 need not update activity metric values 66 eachtime an activity level 62 is determined. In some embodiments, forexample, an IMD 14 may store activity levels 62 within memory, and maydetermine the activity metric values 66 upon receiving a request for thevalues from clinician programmer 20. In alternative examples, an IMD 14may associate activity levels 62 and/or detection of neurological eventswith a current therapy parameter set 60. In addition, a patient 12 maymanually input the occurrence of a neurological event for use by an IMD14.

Further, in some embodiments, as will be described in greater detailbelow, an IMD 14 does not determine the activity metric values 66, butinstead provides activity levels 62 to a computing device, such asclinician programmer 20 or patient programmer 26. In such embodiments,the computing device determines the activity metric values 66 associatedwith each of the therapy parameter sets 60. Additionally, as describedabove, IMD 14 need not determine activity levels 62, but may insteadstore samples of the signals generated by sensors 40. In suchembodiments, the computing device may determine both activity levels 62and activity metric values 66 based on the samples.

FIG. 6 is a block diagram illustrating clinician programmer 20. Aclinician may interact with a processor 80 via a user interface 82 inorder to program therapy for a patient 12, e.g., specify therapyparameter sets. Processor 80 may provide the specified therapy parametersets to an IMD 14 via telemetry circuit 84.

At another time, e.g., during a follow up visit, processor 80 mayreceive information identifying a plurality of therapy parameter sets 60from an IMD 14 via telemetry circuit 84, which may be stored in a memory86. The therapy parameter sets 60 may include the originally specifiedparameter sets, and parameter sets resulting from manipulation of one ormore therapy parameters by a patient 12 using patient programmer 26. Insome embodiments, processor 80 also receives activity metric values 66associated with the therapy parameter sets 60, and stores the activitymetric values in memory 86.

In other embodiments, processor 80 receives activity levels 62associated with the therapy parameter sets 60, and determines values 66of one or more activity metrics for each of the plurality of therapyparameter sets 60 using any of the techniques described above withreference to an IMD 14 and FIGS. 2A, 2B and 4. Processor 80 may, forexample, use threshold values 64 stored in memory 86 to determineactivity metric values 66, as described above. In still otherembodiments, processor 80 receives samples of activity signals from IMD14, and determines activity levels 62 and activity metric values 66based on signals using any of the techniques described above withreference to an IMD 14 and FIGS. 2 and 3.

Upon receiving or determining activity metric values 66, processor 80generates a list of the therapy parameter sets 60 and associatedactivity metric values 66, and presents the list to the clinician. Userinterface 82 may include display 22, and processor 80 may display thelist via display 22. The list of therapy parameter sets 60 may beordered according to the associated activity metric values 66. Where aplurality of activity metric values are associated with each of theparameter sets, the list may be ordered according to the values of theactivity metric selected by the clinician. Processor 80 may also presentother activity information to a user, such as a trend diagram ofactivity over time, or a histogram, pie chart, or other illustration ofpercentages of time that activity levels 62 were within certain ranges.Processor 80 may generate such charts or diagrams using activity levels62 associated with a particular one of the therapy parameter sets 66, orall of the activity levels 62 recorded by IMD 14.

User interface 82 may include display 22 and keypad 24, and may alsoinclude a touch screen or peripheral pointing devices as describedabove. Processor 80 may include a microprocessor, a controller, a DSP,an ASIC, an FPGA, discrete logic circuitry, or the like. Memory 86 mayinclude program instructions that, when executed by processor 80, causeclinician programmer 20 to perform the functions ascribed to clinicianprogrammer 20 herein. Memory 86 may include any volatile, non-volatile,fixed, removable, magnetic, optical, or electrical media, such as a RAM,ROM, CD-ROM, hard disk, removable magnetic disk, memory cards or sticks,NVRAM, EEPROM, flash memory, and the like.

In some embodiments, clinician programmer 20 may be a patient programmerthat is used by a patient 12 to input physical activity, modify physicalactivity stored by an IMD 14, or input neurological events oroccurrences. The input from a patient 12 may be used by an IMD 14 totrack physical activity of a patient 12, neurological events, or anyother physiological event of the patient. An IMD 14 or programmer 20 mayassociate events to stimulation parameters used during those events. Inaddition, the input from a patient 12 may be used to initiate, stop, oradjust electrical stimulation in order to facilitate efficaciousstimulation therapy.

FIG. 7 illustrates an example list 90 of therapy parameter sets andassociated activity metric values 66 that may be presented by clinicianprogrammer 20. Each row of example list 90 includes an identification ofone of therapy parameter sets 60, the parameters of the therapyparameter set, and values 66 associated with the therapy parameter setfor each of two illustrated activity metrics. Programmer 20 may orderlist 90 according to a user-selected one of the activity metrics.

The activity metrics illustrated in FIG. 7 are a percentage of timeactive, and an average number of activity counts per hour. An IMD 14 orprogrammer 20 may determine the average number of activity counts perhour for one of the illustrated therapy parameter sets by identifyingthe total number of activity counts associated with the parameter setand the total amount of time that IMD 14 was using the parameter set. AnIMD 14 or programmer 20 may determine the percentage of time active forone of parameter sets 60 by comparing each activity level 62 associatedwith the parameter set to an “active” threshold, and determining thepercentage of activity levels 62 above the threshold. As illustrated inFIG. 10, an IMD 14 or programmer 20 may also compare each activity levelfor the therapy parameter set to an additional, “high activity”threshold, and determine a percentage of activity levels 62 above thatthreshold.

FIG. 8 is a flow diagram illustrating an example method for displaying alist of therapy parameter sets 60 and associated activity metric values66 that may be employed by a clinician programmer 20. Programmer 20receives information identifying therapy parameter sets 60 andassociated activity levels 62 from an IMD 14 (100). In addition,programmer 20 may receive neurological events from an IMD 14. Programmer20 then determines one or more activity metric values 66 for each of thetherapy parameter sets based on the activity levels 62, and/orneurological events, associated with the therapy parameter sets (102).In other embodiments, an IMD 14 determines the activity metric values66, and provides them to programmer 20, or provides samples of activitysignals associated with therapy parameter sets to programmer 20 fordetermination of activity metric values, as described above. Afterreceiving or determining activity metric values 66, programmer 20presents a list 90 of therapy parameter sets 60 and associated activitymetric values 66 to the clinician, e.g., via display 22 (104).Programmer 20 may order list 90 of therapy parameter sets 60 accordingto the associated activity metric values 66, and the clinician mayselect which of a plurality of activity metrics list 90 is orderedaccording to via a user interface 82 (106).

The invention is not limited to embodiments in which a programmingdevice receives information from the medical device, or presentsinformation to a user. Other computing devices, such as handheldcomputers, desktop computers, workstations, or servers may receiveinformation from the medical device and present information to a user asdescribed herein with reference to programmers 20, 26. A computingdevice, such as a server, may receive information from the medicaldevice and present information to a user via a network, such as a localarea network (LAN), wide area network (WAN), or the Internet. Further,in some embodiments, the medical device is an external medical device,and may itself include user interface and display to present activity orgait information to a user, such as a clinician or patient, forevaluation of therapy parameter sets.

As another example, the invention may be embodied in a trialneurostimulator, which is coupled to percutaneous leads implanted withinthe patient to determine whether the patient is a candidate forneurostimulation, and to evaluate prospective neurostimulation therapyparameter sets. Similarly, the invention may be embodied in a trial drugpump, which is coupled to a percutaneous catheter implanted within thepatient to determine whether the patient is a candidate for animplantable pump, and to evaluate prospective therapeutic agent deliveryparameter sets. Activity metric values collected during use of the trialneurostimulator or pump, which may be related to overall activity andgait, may be used by a clinician to evaluate the prospective therapyparameter sets, and select parameter sets for use by the later implantednon-trial neurostimulator or pump. For example, a trial neurostimulatoror pump may determine values of one or more activity metrics for each ofa plurality of prospective therapy parameter sets, and a clinicianprogrammer may present a list of prospective parameter sets andassociated activity metric values to a clinician. The clinician may usethe list to identify potentially efficacious parameter sets, and mayprogram a permanent implantable neurostimulator or pump for the patientwith the identified parameter sets.

Additionally, the invention is not limited to embodiments in which thetherapy delivering medical device monitors the physiological parametersof the patient described herein. In some embodiments, a separatemonitoring device monitors values of one or more physiologicalparameters of the patient instead of, or in addition to, a therapydelivering medical device. The monitor may include a processor 46 andmemory 48, and may be coupled to or include sensors 40, as illustratedabove with reference to an IMD 14 and FIGS. 2A, 2B and 3. The monitormay determine activity metric values based on the values of themonitored physiological parameter values, or may transmit activitylevels, gait parameters, or the physiological parameter values orsignals to a computing device for determination of the activity metricvalues.

In embodiments in which the medical device determines activity metricvalues, the medical device may identify the current therapy parameterset when a value of one or more activity metric values metrics iscollected, and may associate that value with the therapy parameter set.In embodiments in which a programming device or other computing devicedetermines activity levels, gait parameters, gait freeze events, oractivity metric values, the medical device may associate recordedphysiological parameter values or signals with the current therapyparameter set in the memory. Further, in embodiments in which a separatemonitoring device records physiological parameter values or signals, ordetermines activity levels, gait parameters, gait freeze event, oractivity metric values, the monitoring device may mark recordedphysiological parameter values, activity levels, or activity metricvalues with a current time in a memory, and the medical device may storean indication of a current therapy parameter set and time in a memory. Aprogramming device of other computing device may receive indications ofthe physiological parameter values, activity levels, gait parameters,gait freeze events, or activity metric values and associated times fromthe monitoring device, as well as indications of the therapy parametersets and associated times from the medical device, and may associate thephysiological parameter values, activity levels, gait parameters, gaitfreeze events, or activity metric values with the therapy parameter setthat was delivered by the medical device when the values, parameters,events or levels were recorded.

FIG. 9 is a conceptual diagram illustrating a monitor 110 that monitorsvalues of one or more physiological parameters of the patient insteadof, or in addition to, a therapy delivering medical device as describedabove. Monitor 110 may determine activity metric values, or providephysiological parameter values, activity levels, gait parameters, orgait freeze event information to another device, as described above.

In the illustrated example, monitor 110 is configured to be attached toor otherwise carried by a belt 112, and may thereby be worn by patient12C. FIG. 9 also illustrates various sensors 40 that may be coupled tomonitor 110 by leads, wires, cables, or wireless connections, such asEEG electrodes 114A-C placed on the scalp of patient 12C, a plurality ofEOG electrodes 116A and 116B placed proximate to the eyes of patient12C, and one or more EMG electrodes 118 placed on the chin or jaw thepatient. The number and positions of electrodes 114, 116 and 118illustrated in FIG. 9 are merely exemplary. For example, although onlythree EEG electrodes 174 are illustrated in FIG. 1, an array of between16 and 25 EEG electrodes 114 may be placed on the scalp of patient 12C,as is known in the art. EEG electrodes 114 may be individually placed onpatient 12C, or integrated within a cap or hair net worn by the patient.Signals received from EEG electrodes 114A-C may be analyzed to determinewhether patient 12C is asleep, e.g., using techniques and circuitrydescribed with reference to FIG. 3.

In the illustrated example, patient 12C wears an ECG belt 120. ECG belt120 incorporates a plurality of electrodes for sensing the electricalactivity of the heart of patient 12C. The heart rate and, in someembodiments, ECG morphology of patient 12C may monitored by monitor 110based on the signal provided by ECG belt 120. Examples of suitable belts120 for sensing the heart rate of patient 12C are the “M” and “F” heartrate monitor models commercially available from Polar Electro. In someembodiments, instead of belt 120, patient 12C may wear a plurality ofECG electrodes attached, e.g., via adhesive patches, at variouslocations on the chest of the patient, as is known in the art. An ECGsignal derived from the signals sensed by such an array of electrodesmay enable both heart rate and ECG morphology monitoring, as is known inthe art.

As shown in FIG. 9, patient 12C may also wear a respiration belt 122that outputs a signal that varies as a function of respiration of thepatient. Respiration belt 122 may be a plethysmograpy belt, and thesignal output by respiration belt 122 may vary as a function of thechanges is the thoracic or abdominal circumference of patient 12C thataccompany breathing by the patient. An example of a suitable belt 122 isthe TSD201 Respiratory Effort Transducer commercially available fromBiopac Systems, Inc. Alternatively, respiration belt 122 may incorporateor be replaced by a plurality of electrodes that direct an electricalsignal through the thorax of the patient, and circuitry to sense theimpedance of the thorax, which varies as a function of respiration ofthe patient, based on the signal. In some embodiments, ECG andrespiration belts 120 and 122 may be a common belt worn by patient 12C,and the relative locations of belts 120 and 122 depicted in FIG. 9 aremerely exemplary.

In the example illustrated by FIG. 9, patient 12C also wears atransducer 124 that outputs a signal as a function of the oxygensaturation of the blood of patient 12C. Transducer 124 may be aninfrared transducer. Transducer 124 may be located on one of the fingersor earlobes of patient 12C. Sensors 40 coupled to monitor 110 mayadditionally or alternatively include or be coupled to any of thevariety of sensors 40 described above with reference to FIG. 2 thatoutput signals that vary as a function of patient activity, such as EMGelectrodes, accelerometers, piezoelectric crystals, or pressure sensors.

As discussed above, the overall activity level of a patient, e.g., theextent to which the patient is on his or her feet and moving orotherwise active, rather than sitting or lying in place, may benegatively impacted by any of a variety of ailments or symptoms.Accordingly, the activity level of a patient may reflect the efficacy ofa particular therapy or therapy parameter set in treating the ailment orsymptom. In other words, it may generally be the case that the moreefficacious a therapy parameter set is, the more active the patient willbe. In addition, the activity level of a patient may reflect theefficacy of particular therapy parameters. The activity level may becorrelated with neurological events as an indication of therapy efficacyin treating neurological disorders as described above.

As discussed above, in accordance with the invention, activity levelsmay be monitored during delivery of therapy according to a plurality oftherapy parameter sets, and used to evaluate the efficacy of the therapyparameter sets. As an example chronic pain may cause a patient to avoidparticular activities, high levels of activity, or activity in general.Systems according to the invention may include any of a variety ofmedical devices that deliver any of a variety of therapies to treatchronic pain, such as SCS, DBS, cranial nerve stimulation, peripheralnerve stimulation, or one or more drugs. Systems may use the techniquesof the invention described above to associate activity levels andmetrics with therapy parameter sets for delivery of such therapies, andthereby evaluate the extent to which a therapy parameter set isalleviating chronic pain by evaluating the extent to which the therapyparameter set improves the overall activity level of the patient.

As another example, mood disorders, and particularly depression, maycause a patient to be inactive, despite a physical ability to be active.Often, a patient with depression will spend the significant majority ofhis or her day in bed. Systems according to the invention may includeany of a variety of medical devices that deliver any of a variety oftherapies to treat a mood disorder, such as DBS, cranial nervestimulation, peripheral nerve stimulation, vagal nerve stimulation, orone or more drugs. Systems may use the techniques of the inventiondescribed above to associate activity levels and metrics with therapyparameter sets for delivery of such therapies, and thereby evaluate theextent to which a therapy parameter set is alleviating the mood disorderby evaluating the extent to which the therapy parameter set improves theoverall activity level of the patient.

Movement disorders, such as tremor and Parkinson's disease may alsoaffect the overall activity level of a patient. Further, movementdisorders are also characterized by irregular, uncontrolled andgenerally inappropriate movements, e.g., tremor or shaking, particularlyof the limbs. In addition to using the sensors described above to sensethe overall activity level of a movement disorder patient, someembodiments of the invention may use such sensors to detect the types ofinappropriate movements associated with the movement disorder. Forexample, accelerometers, piezoelectric crystals, or EMG electrodeslocated in the trunk or limbs of a patient may be able to detectinappropriate movements such as tremor. Such detection may also be usedto detect epileptic seizures or symptoms of neurological disorders.

Systems according to the invention may periodically determine the levelor severity of such movements based on the signals output by suchsensors, associate the inappropriate movement levels with currenttherapy parameter sets, and determine activity metric values for therapyparameter sets based on the associated levels. For example, a processorof such a system may determine a frequency or amount of time that suchmovements exceeded a threshold during delivery of a therapy parameterset as an inappropriate movement based activity metric value for thetherapy parameter set. Another type of inappropriate movement associatedparticularly with Parkinson's disease is “gait freeze,” which will bediscussed in greater detail below.

Systems according to the invention may include any of a variety ofmedical devices that deliver any of a variety of therapies to treat amovement disorders (or other neurological disorders), such as DBS,cortical stimulation, or one or more drugs. Systems may use thetechniques of the invention described above to associate overallactivity levels and metrics, as well as inappropriate movement levelsand inappropriate movement activity metrics, with therapy parameter setsfor delivery of such therapies. In this manner, such system may allow auser to evaluate the extent to which a therapy parameter set isalleviating the movement disorder by evaluating the extent to which thetherapy parameter set improves the overall activity level of the patientand/or decreases the extent of inappropriate movements by the patient.

Further, some ailments and symptoms, such as movement disorders andchronic pain, may affect the gait of a patient. More particularly, suchsymptoms and ailments may result in, as examples, an arrhythmic,asymmetric (left leg versus right leg), or unusually variable gait, or agait with relatively short stride lengths. Systems according to theinvention may use sensors discussed above that output signals as afunction of activity, and particularly as a function of footfalls orimpacts, to monitor gait. For example, systems according to theinvention may use one or more accelerometers or piezoelectric crystalslocated on or within the trunk or legs of the patient to monitor gait.

A processor of such a system may periodically determine a value forasymmetry, variability, or stride length of gait, and associate suchvalues with a current therapy parameter set used to deliver any of thetherapies discussed herein with reference to chronic pain or movementdisorders. The processor may determine an activity metric value based ongait by, for example, averaging the gait values associated with atherapy parameter set over a period of time, such as a day, week ormonth. The processor of the system that performs the techniques of theinvention, such as gait monitoring and activity metric determination,may include one or more of a processor of an IMD or a processor of aprogramming or computing device, as discussed above.

As discussed above, the techniques of the invention may be used toevaluate therapy parameter sets used by a medical device to delivertherapy to treat movement disorders, such a Parkinson's disease. Onesymptom that most commonly associated with Parkinson's disease is “gaitfreeze.” Gait freeze may occur when a Parkinson's patient is walkingGait freeze refers to a relatively sudden inability of a Parkinson'spatient to take further steps. Gait freeze is believed to result from aneurological failure and, more specifically, a failure in theneurological signaling from the brain to the legs. A device according toembodiments of the invention may determine when gait freeze has occurredand store information regarding the occurrence of the gait freeze.

FIG. 10 is a block diagram illustrating an example system 10C includingan IMD 14C that collects activity information, including gait regularityinformation, and further identifies and responds to gait freeze events.System 10C and IMD 14C may be substantially similar to systems 10A and10B, and IMDs 14A and 14B, described above. IMD 14C is coupled to leads16F and 16G, which include electrodes 42Q-X. The electrodes may beimplanted, for example, proximate to the spinal cord, or on or withinthe brain of a patient 12, as described above. Although not illustratedin FIG. 10, IMD 14C may include a multiplexer 52 and EEG signal module54.

System 10C, e.g., processor 46 of IMD 14C, may use sensors 40 todetermine activity levels and activity metric values for various therapyparameter sets in the manner discussed above. Additionally, system 130monitors gait regularity, and detects gait freeze events based on thesignals output by sensors 40.

For example, processor 46, or another processor of the system, maydetect a relatively sudden cessation of activity associated with a gaitevent based on the output of accelerometers, piezoelectric crystals, EMGelectrodes, or other sensors that output signals based on footfalls orimpacts associated with, for example, walking. When experiencing a gaitfreeze event, a patient may “rock” or “wobble” while standing in place,as if attempting unsuccessfully to move. In some embodiments, processor46 may monitor any of sensors 40 that output signals as a function ofposture discussed above, such as a 3-axis accelerometer, to detect theminor, rhythmic changes in posture associated with rocking or wobbling.Processor 46 may detect a gait freeze event when it occurs based on oneor more of the posture or activity sensors. For example, processor 46may confirm that a relatively sudden cessation of activity is in fact agait freeze event based on rocking or wobbling indicated by posturesensors.

Further, in some embodiments, the processor may detect a gait freezeprior to onset. For example, sensors 40 may include EMG or EEGelectrodes, and processor 46 may detect a gait freeze prior to onsetbased on irregular EMG or EEG activity. EMG signals, as an example,demonstrate irregularity just prior to a freezing episode, and aprocessor may detect this irregularity as being different from the EMGsignals typically associated with walking. In other words, a walkingpatient may exhibit normal EMG pattern in the legs, which may becontrasted with EMG activity and timing changes that precede freezing.

In general, EMG signals from right and left leg muscles include aregularly alternating rhythm pattern that characterizes normal gait.When the “timing” of the pattern fails, there is no longer a regularrhythm, and a gait freeze may result. Accordingly, a processor maydetect irregularity, variability, or asymmetry, e.g., within and betweenright and left leg muscles, in one or more EMG signals, and may detectan oncoming gait freeze prior to occurrence based on the detection. Insome embodiments, the processor may compare the EMG signals to one ormore thresholds to detect gait freeze. Comparison to a threshold may,for example, indicate an absolute value or increase in irregularity,variability, or asymmetry that exceeds a threshold, indicating anoncoming gait freeze. In some embodiments, thresholds may be determinedbased on EMG signal measurements made when the patient is walkingnormally.

Whether gait freeze is detected prior to or during occurrence, theprocessor may associate the occurrence of the gait freeze event and/orits length with a current therapy parameter set used to control deliveryof a therapy for Parkinson's disease, such as DBS or a drug.Additionally, the processor may determine or update an activity metricvalue for the therapy parameter set based on the gait freeze event, suchas a total number of gait freeze events for the therapy parameter set,an average number of gait freeze events over a period of time, or anaverage length of a gait freeze event.

In some embodiments, in addition to recording gait freeze events anddetermining activity metric values based on such events, the processormay control delivery of a stimulus to terminate the gait freeze event.For example, in embodiments in which leads 16 are implanted on or withinthe brain of the patient, processor 46 may control delivery of atherapeutic stimulation to terminate the gait freeze. Further, inembodiments in which leads 16 are implanted proximate to the spinal cordor peripheral nerves, or within muscle, processor 46 may controldelivery of stimulation perceivable by the patient to “prompt” thepatient to walk, thereby terminating the gait freeze. The stimulationmay be rhythmic, e.g., may approximate the rhythm of walking, which mayprompt the patient to walk and thereby terminate the gait freeze.

In some embodiments, such as embodiments in which leads are not locatedin the above-identified positions, IMD 10C may include a gait cue module130, which processor 46 may control to deliver such stimulation. Gaitcue module 134 may provide stimuli as gait clues, such as audible orotherwise perceivable vibration or electrical stimulus, which may berhythmic. In embodiments in which processor 46 detects a gait freezeprior to onset, the processor may control delivery of such stimuli priorto onset to avoid the occurrence of the gait freeze event.

Further, in some embodiments, a processor of a different device withinsystem 130, such as a processor of a patient programmer 26 (FIG. 1),detects the gait freeze based on signals generated by sensors 40, or anindication received from processor 46 via telemetry circuitry 50. Insuch embodiments, the other device may include a gait cue module 130that provides any of the stimuli or gait cues described above, such asrhythmic audible prompts. Further, a gait cue module 130 in an externalprogrammer or other device may provide other gate cues, such as visualprompts via a display or a projected image of footprints via aprojector. The processor may direct the gait cue module 130 to providesuch gate cues in response to an anticipated or detected gate freezeevent.

FIG. 11 is a flow diagram illustrating an example method for monitoringgait regularity, and identifying and responding to gait freeze events.According to the example method, a processor monitors the gait of apatient based on signals from one or more sensors, such as one or morethree-axis accelerometers or piezoelectric crystals (132). The processormay periodically determine a gait regularity parameter value (134). Thegait regularity parameter value may be a gait asymmetry value, a gaitarrhythmicity value, a gait variability value, or a stride length. Theparameter value may be a numerical value indicative of the extent ofgait regularity, e.g., symmetry, rhythmicity or variability, such as anumber between zero and one, where one indicates normal gait regularity,symmetry, rhythmicity, or variability. The processor may identify atherapy parameter set currently used to deliver therapy to the patientwhen the gait regularity parameter value was determined, such as atherapy for treatment of Parkinson's disease, and may update an activitymetric for the therapy parameter set that reflects gait regularity basedon the determined gait regularity parameter value (136). The gaitregularity activity metric may, for example, by an average of determinedgait parameter values for the therapy parameter set. As other examples,the gait regularity activity metric may reflect comparison of the gaitregularity number, e.g., a symmetry, rhythmicity, or variability number,or a stride length, to a threshold, such as an amount or percentage oftime that the value was above or below a threshold.

If the processor detects a gait freeze event based on signals from theone or more sensors (138), the processor associates the gate freezeevent with a therapy parameter set currently used to control delivery ofa therapy (140). The processor determines or updates a gait freezeactivity metric value for the therapy parameter set based on thedetection (142). The gait freeze activity metric may be, for example, anumber of gait freeze events per unit time that the therapy parameterset was active, such as the number of gait freezes per day.

The processor also controls or requests delivery of stimulation or agait cue to prevent or terminate or overcome the gait freeze event(144). In alternative embodiments, the processor may only identify andlog gait freeze events for review at a later time by a clinician. Inaddition, the gait freeze metric may be reviewed at a later time andused to adjust current therapy programs or create new therapy programs.

FIG. 12 illustrates an example list 145 of therapy parameter sets andassociated activity metric values relating to patient gait which may bepresented by a clinician programmer. Similar to list 90 of FIG. 7, eachrow of example list 145 includes an identification of one of therapyparameter sets 60, the parameters of the therapy parameter set, andactivity metric values 66 associated with the therapy parameter set foreach of two illustrated activity metrics. Programmer 20 may order list145 according to a user-selected one of the activity metrics.

The activity metrics illustrated in FIG. 12 are an average gaitregularity value, expressed as a numerical value between zero and one,and an average number of gait freeze events per day. An IMD 14 orprogrammer 20 may determine the average number of gait freeze events perday for one of the illustrated therapy parameter sets by identifying thetotal number of gait freeze events associated with the parameter set andthe total amount of time that the IMD 14 was using the parameter set.

FIG. 13 is a conceptual diagram illustrating a monitor that monitorsvalues of one or more accelerometers of the patient instead of, or inaddition to, a therapy delivering medical device. As shown in FIG. 13,patient 12D is wearing monitor 146 attached to belt 148. Monitor 146 iscapable of receiving measurements from one or more sensors located on orwithin patient 12D. In the example of FIG. 13, accelerometers 150 and152 are attached to the head and hand of patient 12D, respectively.Accelerometers 150 and 152 may measure movement of the extremities,gait, or general activity level, of patient 12D. Alternatively, more orless accelerometers or other sensors may be used with monitor 146.Accelerometers 150 and 152 may be used to detect gait regularity, gaitfreeze or other movement abnormalities associated with neurologicaldisorders, using the techniques described above. Monitor 146 or someother device may determine any of the variety of activity metricsdescribed above based on the signals generated by accelerometers 150 and152. In some embodiments, accelerometers positioned similarly to themanner illustrated with respect to accelerometers 150 and 152 in FIG. 13may be included in system 10C from FIG. 10.

Accelerometers 150 and 152 may be preferably multi-axis accelerometers,but single-axis accelerometers may be used. As patient 12D moves,accelerometers 150 and 152 detect this movement and send the signals tomonitor 146. High frequency movements of patient 12D may be indicativeof tremor, Parkinson's disease, or an epileptic seizure, and monitor 146may be capable of indicating to an IMD 14, for example, that stimulationtherapy must be changed to effectively treat the patient. Conversely,the sudden stop of movement may indicate gait freeze in patient 12D. Inaddition, accelerometers 150 and 152 may detect the activity of patient12D in addition to or instead of other sensors. Accelerometers 150 and152 may be worn externally, i.e., on a piece or clothing or a watch, orimplanted at specific locations within patient 12D. In addition,accelerometers 150 and 152 may transmit signals to monitor 146 viawireless telemetry or a wired connection.

Monitor 146 may store the measurements from accelerometers 150 and 152in a memory. Monitor 146 may analyze the measurements using any of thetechniques described herein. In some examples, monitor 146 may transmitthe measurements from accelerometers 150 and 152 directly to anotherdevice, such as an IMD 14, programming device 20,26, or other computingdevice. In this case, the other device may analyze the measurements fromaccelerometers 150 and 152 to detect efficacy of therapy or control thedelivery of therapy.

In some examples, a rolling window of time may be used when analyzingmeasurements from accelerometers 150 and 152. Absolute values determinedby accelerometers 150 and 152 may drift with time or the magnitude andfrequency of patient 12D movement may not be determined by a presetthreshold. For this reason, it may be advantageous to normalize andanalyze measurements from accelerometers 150 and 152 over a discretewindow of time. For example, the rolling window may be useful inanalyzing the gait of patient 12D. Movements that stop or are erraticfor a predetermined period of time may be detected in a rolling window.Monitor 146 may even be able to predict gait freeze if a certain patternof movement is detected over a certain time frame defined by the rollingwindow. In this manner, a few quick movements or lack of movement frompatient 12D not associated with gait freeze may not trigger a responseand change in therapy. The rolling window may also be used in detectingchanges in activity with accelerometers 150 and 152 or other sensors asdescribed herein.

FIG. 14 is a flow diagram illustrating monitoring the heart rate andbreathing rate of a patient by measuring cerebral spinal fluid pressure.As shown in FIG. 14, a physiological parameter that may be measured inpatient 12D is heart rate and respiration, or breathing, rate.Specifically, cerebral spinal fluid (CSF) pressure may be analyzed tomonitor the heart rate and breathing rate of patient 12D. A clinicianinitiates a CSF pressure sensor to being monitoring heart rate and/orbreathing rate (154). Alternatively, the CSF pressure sensor may beimplanted within the brain or spinal cord of patient 12D to acquireaccurate pressure signals. The CSF pressure sensor must also store thepressure data or begin to transfer pressure data to an implanted orexternal device. As an example used herein, the CSF pressure sensortransmits signal data to an IMD 14.

Once the CSF pressure sensor is initiated, the CSF pressure sensormeasures CSF pressure and transmits the data to an IMD 14 (156). An IMD14 analyzes the CSF pressure signal to identify the heart rate (158) andbreathing rate (160) of patient 12D. The heart rate and breathing ratecan be identified within the overall CSF pressure signal. Higherfrequency fluctuations (e.g. 40 to 150 beats per minute) can beidentified as the heart rate while lower frequency fluctuations (e.g. 3to 20 breaths per minute) in CSF pressure are the breathing rate. An IMD14 may employ filters, transformations, or other signal processingtechniques to identify the heart rate and breathing rate from the CSFpressure signal.

An IMD 14 may utilize the heart rate and breathing rate information asadditional information when determining the activity metric of patient12D (162). For example, faster heart rates and faster breathing ratesmay indicate that patient 12D is standing or active. IMD 14 may thenstore the activity metric or use it to adjust stimulation therapy (164).

Various embodiments of the invention have been described. However, oneskilled in the art will recognize that various modifications may be madeto the described embodiments without departing from the scope of theinvention. For example, the invention may be embodied as acomputer-readable medium that includes instructions to cause a processorto perform any of the methods described herein. These and otherembodiments are within the scope of the following claims.

What is claimed is:
 1. A method comprising: monitoring, with animplantable medical device, a physiological signal of a patient based ona signal generated by an implantable sensor within the patient;predicting, with the implantable medical device, a gait freeze eventbased on the signal prior to onset of the gait freeze event; andproviding stimulation therapy to the patient from the implantablemedical device in response to the predicted gait freeze event tomitigate the gait freeze event.
 2. The method of claim 1, wherein thesignal generated by the implantable sensor includes an electromyogram(EMG) signal, and wherein predicting, with the implantable medicaldevice, the gait freeze event based on the signal includes predictingthe gait freeze event based on irregular EMG activity in the EMG signal.3. The method of claim 1, wherein the signal generated by theimplantable sensor includes an electroencephalogram (EEG) signal, andwherein predicting, with the implantable medical device, the gait freezeevent based on the signal includes predicting the gait freeze eventbased on irregular EEG activity in the EEG signal.
 4. The method ofclaim 1, wherein predicting, with the implantable medical device, thegait freeze event based on the signal includes: monitoring a gait of thepatient based on the signal generated by the implantable sensor; andanticipating, with the implantable medical device, the gait freeze eventbased on the signal.
 5. The method of claim 1, wherein predicting, withthe implantable medical device, the gait freeze event based on thesignal includes: monitoring a gait of the patient based on the signalgenerated by the implantable sensor; and detecting, with the implantablemedical device, the gait freeze event based on the signal.
 6. The methodof claim 1, wherein providing the stimulation therapy to the patientfrom the implantable medical device in response to the predicted gaitfreeze event to mitigate the gait freeze event includes deliveringstimulation via one or more electrodes to the brain of the patient. 7.The method of claim 1, further comprising providing audible gait cues tothe patient in response to the predicted gait freeze event to mitigatethe gait freeze event.
 8. The method of claim 1, wherein providing thestimulation therapy to the patient from the implantable medical devicein response to the predicted gait freeze event to mitigate the gaitfreeze event includes delivering rhythmic electrical stimulationperceivable by the patient.
 9. The method of claim 1, wherein monitoringthe physiological signal of the patient includes monitoring thephysiological signal of the patient with one or more electrodes of theimplantable sensor.
 10. The method of claim 1, wherein monitoring thephysiological signal of the patient includes sensing activity via anactivity sensor.
 11. The method of claim 1, wherein providing thestimulation therapy to the patient from the implantable medical devicein response to the predicted gait freeze event to mitigate the gaitfreeze event includes modifying at least one of a movement disordertherapy, Parkinson's disease therapy, epilepsy therapy, tremor therapy,or deep brain stimulation being delivered to the patient.
 12. A systemcomprising: an implantable sensor configured to generate a signal thatvaries as a function of activity of a patient; a processor configured tomonitor a physiological signal of a patient based on the signalgenerated by the implantable sensor and predicts a gait freeze eventbased on the signal prior to onset of the gait freeze event; and atherapy module configured to provide stimulation therapy to the patientin response to the predicted gait freeze event to mitigate the gaitfreeze event.
 13. The system of claim 12, wherein the signal generatedby the implantable sensor includes an electromyogram (EMG) signal, andwherein the processor is configured to predict the gait freeze eventbased on irregular EMG activity in the EMG signal.
 14. The system ofclaim 12, wherein the signal generated by the implantable sensorincludes an electroencephalogram (EEG) signal, and wherein the processoris configured to predict the gait freeze event based on irregular EEGactivity in the EEG signal.
 15. The system of claim 12, wherein thetherapy module is configured to deliver rhythmic electrical stimulationperceivable by the patient in response to the predicted gait freezeevent to mitigate the gait freeze event.
 16. The system of claim 12,wherein the therapy module is configured to modify at least one of amovement disorder therapy, Parkinson's disease therapy, epilepsytherapy, tremor therapy, or deep brain stimulation being delivered tothe patient in response to the predicted gait freeze event to mitigatethe gait freeze event.
 17. A non-transitory computer-readable storagemedium comprising instructions that cause a processor to: monitor aphysiological signal of a patient based on a signal generated by animplantable sensor within the patient; predict a gait freeze event basedon the signal prior to onset of the gait freeze event; and issueinstructions to a therapy module to provide stimulation therapy to thepatient from the therapy module in response to the predicted gait freezeevent to mitigate the gait freeze event.
 18. The non-transitorycomputer-readable storage medium of claim 17, wherein the instructionsto the therapy module to provide stimulation therapy to the patient fromthe therapy module in response to the predicted gait freeze event tomitigate the gait freeze event include instructions to deliver rhythmicelectrical stimulation perceivable by the patient.
 19. Thenon-transitory computer-readable storage medium of claim 17, wherein theinstructions to the therapy module to provide stimulation therapy to thepatient from the therapy module in response to the predicted gait freezeevent to mitigate the gait freeze event include instructions to modifyat least one of a movement disorder therapy, Parkinson's diseasetherapy, epilepsy therapy, tremor therapy, or deep brain stimulationbeing delivered to the patient.